Unraveling the Mechanism of Extreme (More than 30 Sigma) Precipitation during August 2018 and 2019 over Kerala, India

Parthasarathi Mukhopadhyay aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Parthasarathi Mukhopadhyay in
Current site
Google Scholar
PubMed
Close
,
Peter Bechtold bEuropean Centre for Medium-Range Weather Forecasts, Reading, United Kingdom

Search for other papers by Peter Bechtold in
Current site
Google Scholar
PubMed
Close
,
Yuejian Zhu cEnvironmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

Search for other papers by Yuejian Zhu in
Current site
Google Scholar
PubMed
Close
,
R. Phani Murali Krishna aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by R. Phani Murali Krishna in
Current site
Google Scholar
PubMed
Close
,
Siddharth Kumar aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Siddharth Kumar in
Current site
Google Scholar
PubMed
Close
,
Malay Ganai aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Malay Ganai in
Current site
Google Scholar
PubMed
Close
,
Snehlata Tirkey aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Snehlata Tirkey in
Current site
Google Scholar
PubMed
Close
,
Tanmoy Goswami aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Tanmoy Goswami in
Current site
Google Scholar
PubMed
Close
,
M. Mahakur aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by M. Mahakur in
Current site
Google Scholar
PubMed
Close
,
Medha Deshpande aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Medha Deshpande in
Current site
Google Scholar
PubMed
Close
,
V. S. Prasad dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by V. S. Prasad in
Current site
Google Scholar
PubMed
Close
,
C. J. Johny eIndia Meteorological Department, Ministry of Earth Sciences, New Delhi, India

Search for other papers by C. J. Johny in
Current site
Google Scholar
PubMed
Close
,
Ashim Mitra eIndia Meteorological Department, Ministry of Earth Sciences, New Delhi, India

Search for other papers by Ashim Mitra in
Current site
Google Scholar
PubMed
Close
,
Raghavendra Ashrit dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by Raghavendra Ashrit in
Current site
Google Scholar
PubMed
Close
,
Abhijit Sarkar dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by Abhijit Sarkar in
Current site
Google Scholar
PubMed
Close
,
Sahadat Sarkar aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Sahadat Sarkar in
Current site
Google Scholar
PubMed
Close
,
Kumar Roy aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Kumar Roy in
Current site
Google Scholar
PubMed
Close
,
Elphin Andrews eIndia Meteorological Department, Ministry of Earth Sciences, New Delhi, India

Search for other papers by Elphin Andrews in
Current site
Google Scholar
PubMed
Close
,
Radhika Kanase aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Radhika Kanase in
Current site
Google Scholar
PubMed
Close
,
Shilpa Malviya aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Shilpa Malviya in
Current site
Google Scholar
PubMed
Close
,
S. Abhilash fCochin University of Science and Technology, Cochin, India

Search for other papers by S. Abhilash in
Current site
Google Scholar
PubMed
Close
,
Manoj Domkawale aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by Manoj Domkawale in
Current site
Google Scholar
PubMed
Close
,
S. D. Pawar aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India

Search for other papers by S. D. Pawar in
Current site
Google Scholar
PubMed
Close
,
Ashu Mamgain dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by Ashu Mamgain in
Current site
Google Scholar
PubMed
Close
,
V. R. Durai eIndia Meteorological Department, Ministry of Earth Sciences, New Delhi, India

Search for other papers by V. R. Durai in
Current site
Google Scholar
PubMed
Close
,
Ravi S. Nanjundiah aIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
gCentre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, India
hDivecha Centre for Climate Change, Indian Institute of Science, Bengaluru, India

Search for other papers by Ravi S. Nanjundiah in
Current site
Google Scholar
PubMed
Close
,
Ashis K. Mitra dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by Ashis K. Mitra in
Current site
Google Scholar
PubMed
Close
,
E. N. Rajagopal dNational Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida, India

Search for other papers by E. N. Rajagopal in
Current site
Google Scholar
PubMed
Close
,
M. Mohapatra eIndia Meteorological Department, Ministry of Earth Sciences, New Delhi, India

Search for other papers by M. Mohapatra in
Current site
Google Scholar
PubMed
Close
, and
M. Rajeevan iMinistry of Earth Sciences, Prithvi Bhawan, New Delhi, India

Search for other papers by M. Rajeevan in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

During August 2018 and 2019 the southern state of India, Kerala, received unprecedented heavy rainfall, which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state-of-the-art global prediction systems: the National Centers for Environmental Prediction (NCEP)-based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), and the Unified Model–based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF). Satellite, radar, and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range; sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the fifth major global reanalysis produced by ECMWF (ERA5) and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column-integrated precipitable water tendency, however, is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward-propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019. Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skillful than the deterministic forecasts, as they were able to predict rainfall anomalies (greater than three standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

Significance Statement

The purpose of this study is to understand and unravel the large-scale mechanism behind the unprecedented heavy rainfall over Kerala, India, during August 2018 and 2019. The study brings out the importance of probabilistic rainfall predictions for extreme heavy rainfall events. The study reveals that large-scale moisture convergence plays a significant role in the extreme rain of August 2018 and 2019. The extreme rainfall of August is associated with a westward-propagating barotropic Rossby wave. The study also demonstrates that ensemble forecasts of extreme rain by the state-of-the-art prediction systems of GFS, IFS, and NCUM are skillful for longer lead times compared to deterministic models and, therefore, can provide better early warnings to the society.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Parthasarathi Mukhopadhyay, mpartha@tropmet.res.in; parthasarathi64@gmail.com

Abstract

During August 2018 and 2019 the southern state of India, Kerala, received unprecedented heavy rainfall, which led to widespread flooding. We aim to characterize the convective nature of these events and the large-scale atmospheric forcing, while exploring their predictability by three state-of-the-art global prediction systems: the National Centers for Environmental Prediction (NCEP)-based India Meteorological Department (IMD) operational Global Forecast System (GFS), the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), and the Unified Model–based NCUM being run at the National Centre for Medium Range Weather Forecasting (NCMRWF). Satellite, radar, and lightning observations suggest that these rain events were dominated by cumulus congestus and shallow convection with strong zonal flow leading to orographically enhanced rainfall over the Ghats mountain range; sporadic deep convection was also present during the 2019 event. A moisture budget analyses using the fifth major global reanalysis produced by ECMWF (ERA5) and forecast output revealed significantly increased moisture convergence below 800 hPa during the main rain events compared to August climatology. The total column-integrated precipitable water tendency, however, is found to be small throughout the month of August, indicating a balance between moisture convergence and drying by precipitation. By applying a Rossby wave filter to the rainfall anomalies it is shown that the large-scale moisture convergence is associated with westward-propagating barotropic Rossby waves over Kerala, leading to increased predictability of these events, especially for 2019. Evaluation of the deterministic and ensemble rainfall predictions revealed systematic rainfall differences over the Ghats mountains and the coastline. The ensemble predictions were more skillful than the deterministic forecasts, as they were able to predict rainfall anomalies (greater than three standard deviations from climatology) beyond day 5 for August 2019 and up to day 3 for 2018.

Significance Statement

The purpose of this study is to understand and unravel the large-scale mechanism behind the unprecedented heavy rainfall over Kerala, India, during August 2018 and 2019. The study brings out the importance of probabilistic rainfall predictions for extreme heavy rainfall events. The study reveals that large-scale moisture convergence plays a significant role in the extreme rain of August 2018 and 2019. The extreme rainfall of August is associated with a westward-propagating barotropic Rossby wave. The study also demonstrates that ensemble forecasts of extreme rain by the state-of-the-art prediction systems of GFS, IFS, and NCUM are skillful for longer lead times compared to deterministic models and, therefore, can provide better early warnings to the society.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Parthasarathi Mukhopadhyay, mpartha@tropmet.res.in; parthasarathi64@gmail.com

Supplementary Materials

    • Supplemental Materials (PDF 214.67 KB)
Save
  • Balsamo, G., P. Viterbo, A. Beljaars, B. van den Hurk, M. Hirschi, A. K. Betts, and K. Scipal, 2009: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system. J. Hydro. Meteor., 10, 623643, https://doi.org/10.1175/2008JHM1068.1.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., N. Semane, P. Lopez, J. Chaboureau, A. Beljaars, and N. Bormann, 2014: Representing equilibrium and nonequilibrium convection in large-scale models. J. Atmos. Sci., 71, 734753, https://doi.org/10.1175/JAS-D-13-0163.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellouin, N., J. Rae, A. Jones, C. Johnson, J. Haywood, and O. Boucher, 2011: Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2-ES and the role of ammonium nitrate. J. Geophys. Res., 116, D20206, https://doi.org/10.1029/2011JD016074.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description—Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677699, https://doi.org/10.5194/gmd-4-677-2011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowler, N. E., A. Arribas, S. E. Beare, K. R. Mylne, and G. J. Shutts, 2009: The local ETKF and SKEB: Upgrades to the MOGREPS short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 135, 767776, https://doi.org/10.1002/qj.394.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buehner, M., J. Morneau, and C. Charette, 2013: Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction. Nonlinear Processes Geophys., 20, 669682, https://doi.org/10.5194/npg-20-669-2013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coumou, D., and S. Rahmstorf, 2012: A decade of weather extremes. Nat. Climate Change, 2, 491496, https://doi.org/10.1038/nclimate1452.

  • Dube, A., R. Ashrit, A. Ashish, K. Sharma, G. R. Iyengar, E. N. Rajagopal, and S. Basu, 2014: Forecasting the heavy rainfall during Himalayan flooding—June 2013. Wea. Climate Extremes, 4, 2234, https://doi.org/10.1016/j.wace.2014.03.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 10161022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Quart. J. Roy. Meteor. Soc., 122, 689719, https://doi.org/10.1002/qj.49712253107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Edwards, J. M., J. Manners, J. C. Thelen, W. J. Ingram, and P. G. Hill, 2018: The radiation code. Unified model documentation paper 23, Met Office, United Kingdom, 55 pp.

  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Forbes, R. M., A. M. Tompkins, and A. Untch, 2011: A new prognostic bulk microphysics scheme for the IFS. ECMWF Tech. Memo. 649, 30 pp., https://doi.org/10.21957/bf6vjvxk.

    • Crossref
    • Export Citation
  • Gregory, D., and P. R. Rowntree, 1990: A mass flux convection scheme with representation of cloud ensemble characteristics and stability dependent closure. Mon. Wea. Rev., 118, 14831506, https://doi.org/10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., and H. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520533, https://doi.org/10.1175/WAF-D-10-05038.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., M. L. Witek, J. Teixeira, R. Sun, H.-L. Pan, J. K. Fletcher, and C. S. Bretherton, 2016: Implementation in the NCEP GFS of a hybrid eddy-diffusivity mass-flux (EDMF) boundary layer parameterization with dissipative heating and modified stable boundary layer mixing. Wea. Forecasting, 31, 341352, https://doi.org/10.1175/WAF-D-15-0053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, J., W. Wang, Y. C. Kwon, S.-Y. Hong, V. Tallapragada, and F. Yang, 2017: Updates in the NCEP GFS cumulus convection schemes with scale and aerosol awareness. Wea. Forecasting, 32, 20052017, https://doi.org/10.1175/WAF-D-17-0046.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joseph, S., and Coauthors, 2015: North Indian heavy rainfall event during June 2013: Diagnostics and extended range prediction. Climate Dyn., 44, 20492065, https://doi.org/10.1007/s00382-014-2291-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kavčič, I., and J. Thuburn, 2018: A Lagrangian vertical coordinate version of the ENDGame dynamical core. Part I: Formulation, remapping strategies, and robustness. Quart. J. Roy. Meteor. Soc., 144, 16491666, https://doi.org/10.1002/qj.3368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., 2012: An evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Ph.D. thesis, Dept. of Atmospheric and Oceanic Science, University of Maryland, College Park, 149 pp.

  • Kleist, D. T., and K. Ide, 2015: An OSSE-based evaluation of hybrid variational–ensemble data assimilation for the NCEP GFS. Part II: 4DEnVar and hybrid variants. Mon. Wea. Rev., 143, 452470, https://doi.org/10.1175/MWR-D-13-00350.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kornhuber, K., and Coauthors, 2019: Extreme weather events in early summer 2018 connected by a recurrent hemispheric wave-7 pattern. Environ. Res. Lett., 14, 054002, https://doi.org/10.1088/1748-9326/ab13bf.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S., A. Hazra, and B. N. Goswami, 2014: Role of interaction between dynamics, thermodynamics and cloud microphysics on summer monsoon precipitating clouds over the Myanmar Coast and the Western Ghats. Climate Dyn., 43, 911924, https://doi.org/10.1007/s00382-013-1909-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, S., and Coauthors, 2018: Implementation of new high resolution NCUM analysis-forecast system in Mihir HPCS. NCMRWF, NMRF/TR/01/2018, 21 pp., http://www.ncmrwf.gov.in/NCUM-Report-Aug2018_final.pdf.

  • Lock, A., A. Brown, M. Bush, G. Martin, and R. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 31873199, https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lott, F., and M. J. Miller, 1997: A new subgrid-scale orographic drag parametrization: Its formulation and testing. Quart. J. Roy. Meteor. Soc., 123, 101127, https://doi.org/10.1002/qj.49712353704.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maheskumar, R. S., and Coauthors, 2014: Mechanism of high rainfall over the Indian west coast region during the monsoon season. Climate Dyn., 43, 15131529, https://doi.org/10.1007/s00382-013-1972-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mamgain, A., A. Sarkar, A. Dube, T. Arulalan, P. Chakraborty, J. P. George, and E. N. Rajagopal, 2018: Implementation of very high resolution (12 km) global ensemble prediction system at NCMRWF and its initial validation. NCMRWF, NMRF/TR/02/2018, 25 pp., http://www.ncmrwf.gov.in/NEPS_TR_Aug2018_Final.pdf.

  • Mishra, S. K., S. Sahany, P. Salunke, I.-S. Kang, and S. Jain, 2018: Fidelity of CMIP5 multi-model mean in assessing Indian monsoon simulations. Climate Atmos. Sci., 1, 39, https://doi.org/10.1038/s41612-018-0049-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mitra, A. K., A. K. Bohra, M. N. Rajeevan, and T. N. Krishnamurti, 2009: Daily Indian precipitation analyses formed from a merged of rain-gauge with TRMM TMPA satellite derived rainfall estimates. J. Meteor. Soc. Japan, 87A, 265279, https://doi.org/10.2151/jmsj.87A.265.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pattanaik, D. R., D. S. Pai, and B. Mukhopadhyay, 2015: Rapid northward progress of monsoon over India and associated heavy rainfall over Uttarakhand: A diagnostic study and real time extended range forecast. Mausam, 66, 551568.

    • Search Google Scholar
    • Export Citation
  • Richardson, D. S., 2000: Skill and relative economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649667, https://doi.org/10.1002/qj.49712656313.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roca, R., M. Viollier, L. Picon, and M. Desbois, 2002: A multi-satellite analysis of deep convection and its moist environment over the Indian Ocean during the winter monsoon. J. Geophys. Res., 107, 8012, https://doi.org/10.1029/2000JD000040.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rotunno, R., and R. A. Houze, 2007: Lessons on orographic precipitation from the Mesoscale Alpine Programme. Quart. J. Roy. Meteor. Soc., 133, 811830, https://doi.org/10.1002/qj.67.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and Coauthors, 2017: Tropical rainfall, Rossby waves and regional winter climate predictions. Quart. J. Roy. Meteor. Soc., 143, 111, https://doi.org/10.1002/qj.2910.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Screen, J. A., and I. Simmonds, 2014: Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Climate Change, 4, 704709, https://doi.org/10.1038/nclimate2271.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shashikanth, K., K. Salvi, S. Ghosh, and K. Rajendran, 2014: Do CMIP5 simulations of Indian summer monsoon rainfall differ from those of CMIP3? Atmos. Sci. Lett., 15, 7985, https://doi.org/10.1002/asl2.466.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shige, S., and C. D. Kummerow, 2016: Precipitation-top heights of heavy orographic rainfall in the Asian monsoon region. J. Atmos. Sci., 73, 30093024, https://doi.org/10.1175/JAS-D-15-0271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, R. N. B., 1990: A scheme for predicting layer clouds and their water contents in a general circulation model. Quart. J. Roy. Meteor. Soc., 116, 435460, https://doi.org/10.1002/qj.49711649210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Soichi, S., and C. D. Kummerow, 2016: Precipitation-Top Heights of Heavy Orographic Rainfall in the Asian Monsoon Region. J. Atmos. Sci., 73, 30093024, https://doi.org/10.1175/JAS-D-15-0271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennant, W. J., and S. Beare, 2014: New schemes to perturb sea-surface temperature and soil moisture content in MOGREPS. Quart. J. Roy. Meteor. Soc., 140, 11501160, https://doi.org/10.1002/qj.2202.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tennant, W. J., G. J. Shutts, A. Arribas, and S. A. Thompson, 2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill. Mon. Wea. Rev., 139, 11901206, https://doi.org/10.1175/2010MWR3430.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Utsav, B., S. M. Deshpande, S. K. Das, and G. Pandithurai, 2017: Statistical characteristics of convective clouds over the Western Ghats derived from weather radar observations. J. Geophys. Res. Atmos., 122, 10 05010 076, https://doi.org/10.1002/2016JD026183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vokoun, M., and M. Hanel, 2018: Comparing ALADIN-CZ and ALADIN-LAEF precipitation forecasts for hydrological modelling in the Czech Republic. Adv. Meteor., 2018, 5368438, https://doi.org/10.1155/2018/5368438.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walters, D., and Coauthors, 2017: The Met Office Unified Model global atmosphere 6.0/6.1 and JULES global land 6.0/6.1 configurations. Geosci. Model Dev., 10, 14871520, https://doi.org/10.5194/gmd-10-1487-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Webster, P. J., V. E. Toma, and H. M. Kim, 2011: Were the 2010 Pakistan floods predictable? Geophys. Res. Lett., 38, L04806, https://doi.org/10.1029/2010GL046346.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M., and G. N. Kiladis, 1999: Convectively coupled equatorial waves: Analysis of clouds and temperature in the wavenumber–frequency domain. J. Atmos. Sci., 56, 374399, https://doi.org/10.1175/1520-0469(1999)056<0374:CCEWAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, D. R., and D. P. Ballard, 1999: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Quart. J. Roy. Meteor. Soc., 125, 16071636, https://doi.org/10.1002/qj.49712555707.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yanai, M., S. Esbensen, and J. H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, P., Q. J. Wang, W. Wu, and Q. Yang, 2020: Which precipitation forecasts to use? Deterministic versus coarser-resolution ensemble NWP models. Quart. J. Roy. Meteor. Soc., 147, 900913, https://doi.org/10.1002/qj.3952.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, Q., and F. H. Carr, 1997: A prognostic cloud scheme for operational NWP models. Mon. Wea. Rev., 125, 19311953, https://doi.org/10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, and D. Kleist, 2016: A comparison of perturbations from an ensemble transform and an ensemble Kalman filter for the NCEP Global Ensemble Forecast System. Wea. Forecasting, 31, 20572074, https://doi.org/10.1175/WAF-D-16-0109.1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Y. Zhu, D. Hou, Y. Luo, J. Peng, and D. Wobus, 2017: Performance of the new NCEP Global Ensemble Forecast System in a parallel experiment. Wea. Forecasting, 32, 19892004, https://doi.org/10.1175/WAF-D-17-0023.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 723 0 0
Full Text Views 763 402 42
PDF Downloads 730 341 38